Knowledge discovery in databases: an overview
AI Magazine
Neo: learning conceptual knowledge by sensorimotor interaction with an environment
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Is it an Agent, or Just a Program?: A Taxonomy for Autonomous Agents
ECAI '96 Proceedings of the Workshop on Intelligent Agents III, Agent Theories, Architectures, and Languages
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
What can i do with this?: finding possible interactions between characters and objects
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Modeling adaptive autonomous agents
Artificial Life
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Autonomous agents are systems situated in dynamic environments. They pursue goals and satisfy their needs by responding to external events from the environment. In these unpredictable conditions, the agents' adaptive skills are a key factor for their success. Based on previous interactions with its environment, an agent must learn new knowledge about it, and use that information to guide its behavior throughout time. In order to build more believable agents, we need to provide them with structures that represent that knowledge, and mechanisms that update them overtime to reflect the agents' experience. Pattern mining, a subfield of data mining, is a knowledge discovery technique which aims to extract previously unknown associations and causal structures from existing data sources. In this paper we propose the use of pattern mining techniques in autonomous agents to allow the extraction of sensory patterns from the agent's perceptions in realtime. We extend some structures used in pattern mining and employ a statistical test to allow an agent of discovering useful information about the environment while exploring it.